Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by late...

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Bibliographic Details
Main Authors Yuan, Haonan, Sun, Qingyun, Fu, Xingcheng, Zhang, Ziwei, Ji, Cheng, Peng, Hao, Li, Jianxin
Format Journal Article
LanguageEnglish
Published 18.11.2023
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